XREAP 2018-06: New Imported Inputs, Wages and Worker Mobility

We provide a comprehensive assessment of the effects of new imported inputs on wage dynamics, on the skill-composition of the labor force, on worker mobility, and on the efficiency of matching between firms and workers. We employ matched employer-employee data for Italy, over 1995-2007. We complement these data with information on the arrival of new imported inputs at the industry level. We find new imported inputs to have a positive effect on average wage growth at the firm level. This effect is driven by two factors: (1) an increase in the white-collar/blue-collar ratio; and (2) an increase in the average wage growth of blue-collar workers, while the wage growth of white collars is not significantly affected. The individual-level analysis reveals that the increase in the average wage of blue collars is driven by the displacement of the lowest paid workers, while continuously employed individuals are not affected. We estimate the unobserved skills of workers following Abowd et al. (1999). We find evidence that new imported inputs lead to a positive selection of higher-skilled workers, and to an improvement in positive assortative matching between firms and workers.

Colantone, I.; Matano, A. (AQR-IREA, XREAP); Naticchioni, P.


XREAP 2018-05: Alternative methods of estimating the longevity risk

The aim of this paper is to estimate the longevity risk and its trend according to the age of the individual. We focus on individuals over 65. We use the value-at-risk to measure the longevity risk. We have proposed the use of an alternative methodology based on the estimation of the truncated cumulative distribution function and the quantiles. We apply a robust estimation method for fitting parametric distributions. Finally, we compare
parametric and nonparametric estimations of longevity risk.




XREAP 2018-04: Tracking economic growth by evolving expectations via genetic programming: A two-step approach

The main objective of this study is to present a two-step approach to generate estimates of economic growth based on agents’ expectations from tendency surveys. First, we design a genetic programming experiment to derive mathematical functional forms that approximate the target variable by combining survey data on expectations about different economic variables. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick (economic growth). In a second step, this set of empirically-generated proxies of economic growth are linearly combined to track the evolution of GDP. To evaluate the forecasting performance of the generated estimates of GDP, we use them to assess the impact of the 2008 financial crisis on the accuracy of agents’ expectations about the evolution of the economic activity in 28 countries of the OECD. While in most economies we find an improvement in the capacity of agents’ to anticipate the evolution of GDP after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden, Austria and Finland.

Claveria, O. (AQR-IREA, XREAP); Monte, E.; Torra, S. (RISKCENTER, XREAP)